Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data

The development of single cell transcriptome sequencing has allowed researchers the possibility to dig inside the role of the individual cell types in a plethora of disease scenarios. It also expands to the whole transcriptome what before was only possible for a few tenths of antibodies in cell population analysis. More importantly, it allows resolving the permanent question of whether the changes observed in a particular bulk experiment are a consequence of changes in cell type proportions or an aberrant behavior of a particular cell type. However, single cell experiments are still complex to perform and expensive to sequence making bulk RNA-Seq experiments yet more common. scRNA-Seq data is proving highly relevant information for the characterization of the immune cell repertoire in different diseases ranging from cancer to atherosclerosis. In particular, as scRNA-Seq becomes more widely used, new types of immune cell populations emerge and their role in the genesis and evolution of the disease opens new avenues for personalized immune therapies. Immunotherapy have already proven successful in a variety of tumors such as breast, colon and melanoma and its value in other types of disease is being currently explored. From a statistical perspective, single-cell data are particularly interesting due to its high dimensionality, overcoming the limitations of the "skinny matrix" that traditional bulk RNA-Seq experiments yield. With the technological advances that enable sequencing hundreds of thousands of cells, scRNA-Seq data have become especially suitable for the application of Machine Learning algorithms such as Deep Learning (DL). We present here a DL based method to enumerate and quantify the immune infiltration in colorectal and breast cancer bulk RNA-Seq samples starting from scRNA-Seq. Our method makes use of a Deep Neural Network (DNN) model that allows quantification not only of lymphocytes as a general population but also of specific CD8+, CD4Tmem, CD4Th and CD4Tregs subpopulations, as well as B-cells and Stromal content. Moreover, the signatures are built from scRNA-Seq data from the tumor, preserving the specific characteristics of the tumor microenvironment as opposite to other approaches in which cells were isolated from blood. Our method was applied to synthetic bulk RNA-Seq and to samples from the TCGA project yielding very accurate results in terms of quantification and survival prediction.This work was supported by the European Union’s Horizon 2020 research and innovation program under grant agreement number 633592 (Project APERIM: Advanced bioinformatics platform for personalized cancer immunotherapy). We thank Francesca Finotello and Zlatko Trajanoski for fruitful discussions and to the CNIC Bioinformatics Unit members for continuous support and work. The CNIC is supported by MEIC and the ProCNIC Foundation, and is a Severo Ochoa Center of Excellence (MEIC award SEV-2015-0505).S

dSreg: a Bayesian model to integrate changes in splicing and RNA-binding protein activity

MOTIVATION: Alternative splicing (AS) is an important mechanism in the generation of transcript diversity across mammals. AS patterns are dynamically regulated during development and in response to environmental changes. Defects or perturbations in its regulation may lead to cancer or neurological disorders, among other pathological conditions. The regulatory mechanisms controlling AS in a given biological context are typically inferred using a two-step framework: differential AS analysis followed by enrichment methods. These strategies require setting rather arbitrary thresholds and are prone to error propagation along the analysis. RESULTS: To overcome these limitations, we propose dSreg, a Bayesian model that integrates RNA-seq with data from regulatory features, e.g. binding sites of RNA-binding proteins. dSreg identifies the key underlying regulators controlling AS changes and quantifies their activity while simultaneously estimating the changes in exon inclusion rates. dSreg increased both the sensitivity and the specificity of the identified AS changes in simulated data, even at low read coverage. dSreg also showed improved performance when analyzing a collection of knock-down RNA-binding proteins' experiments from ENCODE, as opposed to traditional enrichment methods, such as over-representation analysis and gene set enrichment analysis. dSreg opens the possibility to integrate a large amount of readily available RNA-seq datasets at low coverage for AS analysis and allows more cost-effective RNA-seq experiments. AVAILABILITY AND IMPLEMENTATION: dSreg was implemented in python using stan and is freely available to the community at https://bitbucket.org/cmartiga/dsreg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This work was supported by grants from the European Union [CardioNeTITN-289600, CardioNext-608027]; the Spanish Ministry of Economy and Competitiveness [SAF2015-65722-R, SAF2012-31451]; the Instituto de salud Carlos III (ISCIII) [CPII14/00027, RD012/0042/0066]; the Madrid Regional Government [2010-BMD-2321 “Fibroteam”]. The study also received support from the Plan Estatal de I+D+I 2013-2016 – European Regional Development Fund (ERDF) “A way of making Europe”, Spain. The CNIC is supported by the Spanish Ministry of Economy, Industry and Competitiveness and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence (MEIC award SEV-2015-0505).S

CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis

CARMAweb (Comprehensive R-based Microarray Analysis web service) is a web application designed for the analysis of microarray data. CARMAweb performs data preprocessing (background correction, quality control and normalization), detection of differentially expressed genes, cluster analysis, dimension reduction and visualization, classification, and Gene Ontology-term analysis. This web application accepts raw data from a variety of imaging software tools for the most widely used microarray platforms: Affymetrix GeneChips, spotted two-color microarrays and Applied Biosystems (ABI) microarrays. R and packages from the Bioconductor project are used as an analytical engine in combination with the R function Sweave, which allows automatic generation of analysis reports. These report files contain all R commands used to perform the analysis and guarantee therefore a maximum transparency and reproducibility for each analysis. The web application is implemented in Java based on the latest J2EE (Java 2 Enterprise Edition) software technology. CARMAweb is freely available at

Axial skeleton anterior-posterior patterning is regulated through feedback regulation between Meis transcription factors and retinoic acid.

Vertebrate axial skeletal patterning is controlled by co-linear expression of Hox genes and axial level-dependent activity of HOX protein combinations. MEIS transcription factors act as co-factors of HOX proteins and profusely bind to Hox complex DNA; however, their roles in mammalian axial patterning remain unknown. Retinoic acid (RA) is known to regulate axial skeletal element identity through the transcriptional activity of its receptors; however, whether this role is related to MEIS/HOX activity remains unknown. Here, we study the role of Meis in axial skeleton formation and its relationship to the RA pathway in mice. Meis elimination in the paraxial mesoderm produces anterior homeotic transformations and rib mis-patterning associated to alterations of the hypaxial myotome. Although Raldh2 and Meis positively regulate each other, Raldh2 elimination largely recapitulates the defects associated with Meis deficiency, and Meis overexpression rescues the axial skeletal defects in Raldh2 mutants. We propose a Meis-RA-positive feedback loop, the output of which is Meis levels, that is essential to establish anterior-posterior identities and patterning of the vertebrate axial skeleton.This research was supported by the Ministerio de Ciencia, Innovación y Universidades (PGC2018-096486-B-I00), the Instituto de Salud Carlos III (RD16/ 0011/0019) and by the Comunidad de Madrid (S2017/BMD3875). The Centro Nacional de Investigaciones Cardiovasculares Carlos III is supported by the Ministerio de Ciencia, Innovación y Universidades and the Pro Centro Nacional de Investigaciones Cardiovasculares Carlos III Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). A.C.L.-D. was the recipient of a Formación Personal Investigador fellowship from the Ministerio de Economı́a y Competitividad (BES-2013-064374).S

PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways

While generation of high-throughput expression data is becoming routine, the fast, easy, and systematic presentation and analysis of these data in a biological context is still an obstacle. To address this need, we have developed PathwayExplorer, which maps expression profiles of genes or proteins simultaneously onto major, currently available regulatory, metabolic and cellular pathways from KEGG, BioCarta and GenMAPP. PathwayExplorer is a platform-independent web server application with an optional standalone Java application using a SOAP (simple object access protocol) interface. Mapped pathways are ranked for the easy selection of the pathway of interest, displaying all available genes of this pathway with their expression profiles in a selectable and intuitive color code. Pathway maps produced can be downloaded as PNG, JPG or as high-resolution vector graphics SVG. The web service is freely available at ; the standalone client can be downloaded at

A quantization method based on threshold optimization for microarray short time series

BACKGROUND: Reconstructing regulatory networks from gene expression profiles is a challenging problem of functional genomics. In microarray studies the number of samples is often very limited compared to the number of genes, thus the use of discrete data may help reducing the probability of finding random associations between genes. RESULTS: A quantization method, based on a model of the experimental error and on a significance level able to compromise between false positive and false negative classifications, is presented, which can be used as a preliminary step in discrete reverse engineering methods. The method is tested on continuous synthetic data with two discrete reverse engineering methods: Reveal and Dynamic Bayesian Networks. CONCLUSION: The quantization method, evaluated in comparison with two standard methods, 5% threshold based on experimental error and rank sorting, improves the ability of Reveal and Dynamic Bayesian Networks to identify relations among genes

Bayesian Inference of Gene Expression

Omics techniques have changed the way we depict the molecular features of a cell. The integrative and quantitative analysis of omics data raises unprecedented expectations for understanding biological systems on a global scale. However, its inherently noisy nature, together with limited knowledge of potential sources of variation impacting health and disease, require the use of proper mathematical and computational methods for its analysis and integration. Bayesian inference of probabilistic models allows propagation of the uncertainty from the experimental data to our beliefs of the model parameters, allowing us to appropriately answer complex biological questions. In this chapter, we build probabilistic models of gene expression from RNA-seq data and make inference about their parameters using Bayesian methods. We present models of increasing complexity, from the quantification of a single gene expression to differential gene expression for a whole transcriptome, comparing them to the available tools for analysis of gene expression data. We provide Stan scripts that introduce the reader into the implementation of Bayesian statistics for omics data. The rationale that we apply for transcriptomics data may be easily extended to model the particularities of other omics data and to integrate the different regulatory layers.FS-C received support from the Spanish Ministerio de Economía y Competitividad [grant no. RTI2018-102084-B-I00]; EL-P received support from the Spanish Ministerio de Economía y Competitividad (RTI2018-096961-B-I00), from the European Union (CardioNeT-ITN-289600 and CardioNext-ITN-608027) and the Spanish Carlos III Institute of Health (RD12/0042/066); M.A.d.P received support from the Spanish Ministerio de Economía y Competitividad (SAF2017-83130-R) and from the European Union Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement nº 641639 BIOPOL-ITN-641639; V.J-J. received an ESR contract from BIOPOL-ITN-641639). M.A.d.P is member of the Tec4Bio consortium (ref. S2018/NMT4443). The CNIC is supported by MCIU and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence [MCIU award SEV-2015-0505].S

Embryological-origin-dependent differences in homeobox expression in adult aorta: role in regional phenotypic variability and regulation of NF-κB activity

OBJECTIVE: Different vascular beds show differing susceptibility to the development of atherosclerosis, but the molecular mechanisms underlying these differences are incompletely understood. This study aims to identify factors that contribute to the phenotypic heterogeneity of distinct regions of the adult vasculature. APPROACH AND RESULTS: High-throughput mRNA profiling in adult mice reveals higher expression of the homeobox paralogous genes 6 to 10 (Hox6-10) in the athero-resistant thoracic aorta (TA) than in the athero-susceptible aortic arch (AA). Higher homeobox gene expression also occurs in rat and porcine TA, and is maintained in primary smooth muscle cells isolated from TA (TA-SMCs) compared with cells from AA (AA-SMCs). This region-specific homeobox gene expression pattern is also observed in human embryonic stem cells differentiated into neuroectoderm-SMCs and paraxial mesoderm-SMCs, which give rise to AA-SMCs and TA-SMCs, respectively. We also find that, compared with AA and AA-SMCs, TA and TA-SMCs have lower activity of the proinflammatory and proatherogenic nuclear factor-κB (NF-κB) and lower expression of NF-κB target genes, at least in part attributable to HOXA9-dependent inhibition. Conversely, NF-κB inhibits HOXA9 promoter activity and mRNA expression in SMCs. CONCLUSION: Our findings support a model of Hox6-10-specified positional identity in the adult vasculature that is established by embryonic cues independently of environmental factors and is conserved in different mammalian species. Differential homeobox gene expression contributes to maintaining phenotypic differences between SMCs from athero-resistant and athero-susceptible regions, at least in part through feedback regulatory mechanisms involving inflammatory mediators, for example, reciprocal inhibition between HOXA9 and NF-κB.The authors’ laboratories Sources of Funding supported are by grants from the Ministerio de Economía y Competitividad (MINECO; SAF201016044), Instituto de Salud Carlos III (ISCIII; RD/06/0014/0021, RD12/0042/0028), the Belgian Society of Cardiology (Dr. Léon Dumont Prize 2010), the European Commission (Liphos-317916), the Wellcome Trust (WT078390MA), and the Cambridge Biomedical Research Center. C. Cheung was sponsored by the Agency for Science, Technology and Research (Singapore). J.M. GonzálezGranado and P. Fernández received salary support from the ISCIII (CP11/00145 and CD07/00021, respectively). The CNIC is supported by MINECO and Pro-CNIC Foundation.S

miRNA profiling during antigen-dependent T cell activation: A role for miR-132-3p

microRNAs (miRNAs) are tightly regulated during T lymphocyte activation to enable the establishment of precise immune responses. Here, we analyzed the changes of the miRNA profiles of T cells in response to activation by cognate interaction with dendritic cells. We also studied mRNA targets common to miRNAs regulated in T cell activation. pik3r1 gene, which encodes the regulatory subunits of PI3K p50, p55 and p85, was identified as target of miRNAs upregulated after T cell activation. Using 3'UTR luciferase reporter-based and biochemical assays, we showed the inhibitory relationship between miR-132-3p upregulation and expression of the pik3r1 gene. Our results indicate that specific miRNAs whose expression is modulated during T cell activation might regulate PI3K signaling in T cells.We thank Miguel Vicente-Manzanares for help with English editing and Almudena R. Ramiro for helpful discussions. We appreciate help from Gloria Martinez del Hoyo on DCs experiments set up. We also thank the CNIC Genomics, Bioinformatics and Cellomics Units for technical support. This work was supported by grants SAF2014-55579R from Ministerio de Economia y Competitividad-Spain, ERC-2011-AdG 294340-GENTRIS, CIBER CARDIOVASCULAR (FEDER and Instituto de Salud Carlos III), PIE-13-00041 and INDISNET S2011-BMD-2332 (F.S.M.). The Centro Nacional de Investigaciones Cardiovasculares (CNIC, Spain) is supported by the Ministerio de Economia y Competitividad-Spain and the Pro-CNIC Foundation.S